InferenceSaver vs OpenRouter
A unified API for many models — compared with routing plus cost control.
InferenceSaver and OpenRouter both hand you one API key and one endpoint for a lot of models, and that part really is the same on either side. See how a unified API gateway powered our own competitive research. The differences start the moment more than one person is spending against the same balance.
Who spent what, on which model, and what the request actually cost ? see how we built competitive intelligence.
Whether one balance can be split across people and services without everyone sharing a single key.
Whether image, video, and audio bill from that same balance or from a second vendor.
Why teams move their spend to InferenceSaver
Watching the bill climb is not a strategy. These are the three things that change on day one.
Spend you can attribute
Every request carries its model, its list rate, and what you actually paid — grouped by key and by member. Example traces live in our observability guide.
One endpoint, many providers
Frontier and open models behind a single OpenAI-compatible request shape, so switching is configuration. See what models we route.
Media on the same balance
Image, video, and audio generation bill from the credits that already cover your text calls ? try Media Studio.
Those are the differences worth putting side by side. Browse our model catalogue before you compare. The table below answers each one with a verdict rather than a paragraph, so a row can be read in a glance — and it keeps the rows where OpenRouter comes out ahead instead of leaving them out.
Where the two products do the same thing, the row says so rather than being quietly dropped.
OpenRouter carries the broader model catalogue, and the table gives it to them.
Every row is something you can check against either product's own documentation ? ours lives in the docs.
Side by side
The axes that actually change the decision, with no attempt to hide the ones where OpenRouter is strong.
Primary job
InferenceSaverCost controlOpenRouterModel breadthCost visibility
InferenceSaverPer requestOpenRouterPer modelTeam controls
InferenceSaverWorkspacesOpenRouterAccount levelBeyond text
InferenceSaverIncludedOpenRouterNot offeredOpenAI-compatible API
InferenceSaverYesOpenRouterYesPublished per-model rates
InferenceSaverYesOpenRouterYesModel catalogue breadth
InferenceSaverFocusedOpenRouterBroaderSpend attributed per API key
InferenceSaverYesOpenRouterAccount levelSpend attributed per member
InferenceSaverYesOpenRouterNot offeredWorkspace roles
InferenceSaverYesOpenRouterNot offeredScoped API keys
InferenceSaverYesOpenRouterAccount keysImage generation
InferenceSaverIncludedOpenRouterNot offeredVideo generation
InferenceSaverIncludedOpenRouterNot offeredAudio generation
InferenceSaverIncludedOpenRouterNot offered
A one-word verdict is a claim, not evidence. The three sections below take the rows that decide most migrations and show them where they actually happen: in a traced request, at the endpoint you integrate against, and on the Media Studio bill for everything that is not text.
Observability — what a single request records once it has run, shown in request traces.
Model access — the endpoint you keep if you move, and what changes around it.
Media — where image, video, and audio spend ends up.
Where the difference actually shows up
Three places the gap is visible in a workflow rather than a feature list.
Every request, with its real cost attached
Each call is traced with the model, the original provider rate, and the discounted rate side by side, grouped by API key and by workspace member. A shared balance still answers who spent what — which is the question that becomes urgent the moment inference stops being a rounding error.
| Capability | InferenceSaver | |
|---|---|---|
| Per-request cost trace | Yes | Dashboard totals |
| Attribution per API key | Yes | Account level |
| Attribution per member | Yes | Not offered |
| Original vs discounted rate | Side by side | List rate |
One OpenAI-compatible endpoint across providers
Frontier and open models sit behind the same request shape, so moving is a base URL and a key rather than a rewrite. OpenRouter lists a wider catalogue than we do; where we differ is what surrounds the endpoint once more than one person is spending against it.
| Capability | InferenceSaver | |
|---|---|---|
| OpenAI-compatible API | Yes | Yes |
| Catalogue breadth | Focused | Broader |
| Published per-model rates | Yes | Yes |
| Scoped keys per workspace | Yes | Account keys |
Image, video and audio on the same balance
Generation runs in the same studio and draws from the same credits as text, so a product that ships mixed media does not need a second vendor, a second invoice, and a second place to look when the bill moves.
| Capability | InferenceSaver | |
|---|---|---|
| Image generation | Included | Not offered |
| Video generation | Included | Not offered |
| Audio generation | Included | Not offered |
| Shared credit balance | Yes | Text only |
None of it matters until it reaches an invoice. What follows is the billing shape of each product — how you are charged, not what any one model costs today. Check current model rates for the latest. Published rates move, and a comparison page that quotes them is wrong within a week of shipping.
How each side prices a request, and what that price is measured against.
What a shared team balance can attribute, and what it cannot.
Which modalities draw from the same credits.
What you actually pay
The billing differences that show up on an invoice, not in a feature list.
Pricing model
InferenceSaverDiscountedOpenRouterList rateCost per request
InferenceSaverTracedOpenRouterPublishedTeam billing
InferenceSaverPer memberOpenRouterAccount levelImage, video and audio
InferenceSaverSame balanceOpenRouterNot offeredAPI compatibility
InferenceSaverYesOpenRouterYes
Pricing on our models
See how our pricing compares across the models you actually use. Every request shows the provider rate and your discounted rate side by side.
| Rank | Model | Capability | Context | Max Output | Input Tokens | Output Tokens | Savings | |
|---|---|---|---|---|---|---|---|---|
| #1 | Claude Fable 5 claude-fable-5 | Chat | 200K | 16.4K | $20.00$10.00per 1M tokens | $100.00$50.00per 1M tokens | up to 70% off | |
| #2 | Claude Fable 5 1 claude-fable-5-1 | Chat | 200K | 16.4K | $20.00$10.00per 1M tokens | $100.00$50.00per 1M tokens | up to 70% off | |
| #3 | Haiku 4.5.20251001 claude-haiku-4-5-20251001 | Chat | 200K | 8.2K | $2.00$1.00per 1M tokens | $10.00$5.00per 1M tokens | up to 70% off | |
| #4 | Opus 4.6 claude-opus-4-6 | Chat | 200K | 32K | $10.00$5.00per 1M tokens | $50.00$25.00per 1M tokens | up to 70% off | |
| #5 | Opus 4.7 claude-opus-4-7 | Chat | 200K | 32K | $10.00$5.00per 1M tokens | $50.00$25.00per 1M tokens | up to 70% off |
Every model above shows the provider list rate and what you actually pay — the savings column is where the difference is visible. Browse all 5 models to see the full catalogue with per-model details, endpoint compatibility, and the same side-by-side pricing.
Can't decide if we're the right choice?
Ask the AI assistant you already trust about InferenceSaver and get an independent comparison.
Still got questions? Ask your favourite AI if InferenceSaver makes sense for you.
Questions teams ask
Do I have to rewrite my integration?
No. The gateway is OpenAI-compatible, so in most cases it is a base URL and an API key change. If you already call OpenRouter through an OpenAI-compatible client, the same client works here. See the API docs for the exact shape.
Can I keep using OpenRouter as well?
Yes. Nothing here asks for exclusivity, and teams commonly route part of their traffic through each while they compare real bills over a few weeks ? the same pattern we used for competitive keyword research.
How is spend attributed across a team?
Requests are traced per API key and per workspace member, so a shared balance still shows who spent what, on which model, at which cost.
Is media generation billed separately?
No. Image, video, and audio generation draw from the same credit balance as text, which is the reason teams running mixed workloads tend to consolidate.
Get in touch with our team
Book a demo to discover how InferenceSaver can improve your AI operations, model costs, and production workflows.
“We finally have an AI stack that the product and engineering teams can use without switching between providers.”
Priya Nair
AI Operations Lead
Up to 2x usage throughput on the same spend
400ms median time to first response from frontier models
60-80% lower inference costs without sacrificing latency
We will map your current setup, recommend the right plan, and show a clear path to rollout.